Hugging Face Trending Papers
Jul 21
Exposure-Based Reinforcement Learning to Rank
Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e. g.
Exposure-Based Reinforcement Learning to Rank
arXiv:2607. 18689v1 Announce Type: new Abstract: Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.
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arXiv AI
Aug 3
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR
arXiv:2601. 09361v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models.
arXiv AI
Jun 9
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR
arXiv:2606. 08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for the current policy.
arXiv Machine Learning
Aug 7
RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction
arXiv:2608. 06310v1 Announce Type: new Abstract: Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models.
arXiv Machine Learning
Jun 29
Learning to Evict from Key-Value Cache
arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.
arXiv AI
Jun 3
Efficient Hyperparameter Optimization for LLM Reinforcement Learning
arXiv:2606. 03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive.
arXiv AI
Jul 9
Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
arXiv:2607. 07693v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences.
arXiv AI
Jul 7
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
arXiv:2607. 04713v1 Announce Type: cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks.
arXiv AI
Jul 7
ICR-RL: Deep Reinforcement Learning via In-Context Regression
arXiv:2509. 11259v2 Announce Type: replace-cross Abstract: Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, related tasks.
arXiv Machine Learning
Jun 16
GD$^2$PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization
arXiv:2606. 16771v1 Announce Type: new Abstract: As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities.
arXiv AI
Jul 24
Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation
arXiv:2607. 20908v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for optimized code generation.
Hugging Face Trending Papers
Jul 23
Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for optimized code generation. However, existing RLVR approaches primarily rely on outcome-based signals such as correctness and speedup, overlooking performance-critical structural properties of programs that are essential for generating optimized code.